416 citations · 850 across the 17 of their papers we have counts for
5 papers · 1 filter
Graph Communal Contrastive Learning
Bolian Li, Baoyu Jing, Hanghang Tong
Graph representation learning is crucial for many real-world applications (e.g. social relation analysis). A fundamental problem for graph representation learning is how to effecti…
Multiplex Graph Neural Network for Extractive Text Summarization
Baoyu Jing, Zeyu You, Tao Yang +2
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, senten…
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
Baoyu Jing, Shengyu Feng, Yuejia Xiang +3
Graphs are powerful representations for relations among objects, which have attracted plenty of attention. A fundamental challenge for graph learning is how to train an effective G…
HDMI: High-order Deep Multiplex Infomax
Baoyu Jing, Chanyoung Park, Hanghang Tong
Networks have been widely used to represent the relations between objects such as academic networks and social networks, and learning embedding for networks has thus garnered plent…
Network of Tensor Time Series
Baoyu Jing, Hanghang Tong, Yada Zhu
Co-evolving time series appears in a multitude of applications such as environmental monitoring, financial analysis, and smart transportation. This paper aims to address the follow…